The main concept of SE is to estimate states of the network (e.g., voltage, active and reactive power) that are not measured. In practice, one estimates the network's state by minimizing the weighted least squares of the difference between the measurement and variable values. If the problem is well formulated, then Gauss-Newton algorithms can find a solution efficiently. To formulate the problem well, accurate models for the electrical distribution networks are required. Distribution system state estimation (DSSE) has been proposed to handle the unbalanced systems recently applied in [22] summarized in Appendix 2. In Australia, most of the electricity distribution networks are radial (no ‘loops’ in the distribution network). Typically, the nodal power injections and branch power flow in a network are considered as constant real and reactive power over the time interval (typically 5 or 30 minutes) [19]–[21]. Here, the power flow and state estimation should consider two main constraints (i.e., voltage and thermal constraints) and the existing active energy management strategies (i.e., volt/var, volt/watt, etc.). Based on the reviewed projects, most typically use balanced power flow to calculate hosting capacity, which neglects the phase imbalance that may lead to over- or under-estimating the hosting capacity results and the ability of the network to accommodate DERs. One of the projects developed unbalanced distribution system state estimation (DSSE) technique to calculate the hosting capacity [22]. The state estimation technique needs less input quantities than that required in power flow. However, it is sensitive to the accuracy of the data, with particular high non-linearity in the data increases the risk of numerical instability in the solution finding process. Both power flow and state estimation need explicit models for the network components, including phase imbalance effects to calculate hosting capacity accurately. Active network management strategies can be defined for both voltage and thermal constraints using centralized, decentralized, and distributed techniques [21]. It is worthy of mentioning that the concepts of hosting capacity and active network management strategies are often profoundly related. An active network management strategy often implicitly or explicitly determines the DER hosting capacity achieved within a given distribution network segment. Active network management strategies focus on providing setpoint control for DER by determining the exact value of the DER output that will best allow some operational objective to be achieved.
4.2.4 Hosting Capacity Calculation Methods There are several hosting capacity methodologies under development and more likely on the horizon. For ease of discussion, we have focused on four primary methodological categories recommended and discussed by EPRI: stochastic, streamlined, iterative, and hybrid. These methods are briefly summarized in the main body of the report with illustrating their accuracy and comparison for assessing hosting capacity based on distribution feeder and DER evaluation criteria. For more details, the reader can be referred to [14]. Additionally, a capacity constraint-based method can be used to approximate hosting capacity, which can be a useful if only approximate or estimated data is available for many measurements critical for assessing hosting capacity.
Stochastic Methods This method starts with a model of the existing distribution system, performing a baseline power flow analysis of the existing system and gradually increasing the penetration level of DERs on a feeder for varying sizes and random locations to evaluate any adverse effects arises for different scenarios that results in hosting capacity range. Assumptions such as DERs with similar characteristics, sizes and 49
